Archive for the ‘seo news’ Category
Saturday, September 16th, 2023
Google is on trial for allegedly using underhand tactics to ensure it stays the world’s leading search engine.
The U.S. Justice Department claims Google, which owns a 90% market share in search, paid massive sums to companies like Apple to make it the default search engine on products like the iPhone.
These multibillion-dollar deals gave Google an unfair advantage, the DOJ alleges, making it nearly impossible for rival companies to compete.
The trial will last 10 weeks and include testimonies from key figures like Alphabet and Google CEO Sundar Pichai.
The outcome of the landmark case could bring significant changes to Google and the future of the Internet. But it’s equally likely the trial will result in no changes and Google will be free to continue operating however it wants.
We’ll keep updating this article with the latest developments from this landmark trial.
As the trial is set to cover many Google search-related issues, we have organized the updates by topic to make the timeline easier to follow.
Google credits its 90% market share to being a superior platform (Sept. 12)
- John Schmidtlein, lead lawyer for Google, claims the company dominates the search market due to being a superior product.
- Google argues that users can easily switch to rival search engines even if it’s the default.
- Antonio Rangel, a California Institute of Technology economist, testified that Google’s defaults discourage users from switching, saying switching to a different search engine is not easy.
- He cited an example where switching to Bing from Google on an Android 12 phone required 10 steps, describing it as “considerable choice friction”, reports Business Insider.
Google ‘hid and destroyed evidence’ (Sept. 12)
- Justice Department attorney Kenneth Dintzer accused Google of “hiding and destroying documents because they knew they were violating the antitrust laws”, reports Bloomberg.
- In his opening statement on day one, Dintzer presented evidence to show that Google was knowingly breaking laws.
- He pointed to an October 2021 chat message from CEO Pichai, which read: “Need the link for my leaders circle tomorrow…can we change the setting of this group to history off… thanks.”
- When history is off, conversations are auto-deleted after 24 hours.
- Google declined to comment.
Apple allegedly didn’t want a default search engine (Sept. 12)
- The DOJ revealed that Apple intended to provide users with a choice screen to select between Google and Yahoo as their search engine.
- However, Google rejected this proposal with the statement “No default placement, no revenue share,” as stated in an email.
- Kenneth Dintzer, the lead attorney for the DOJ, characterized Google’s response as a monopolistic action.
Google pays $10 billion a year to maintain default status (Sept. 12)
- Justice Department attorney Dintzer accused Google of recognizing the important of default status and said this was the reason why the company spent more than $10 billion a year to brands like Apple.
- Dintzer added that ” this wheel has been turning for more than 12 years and it always turns to Google’s advantage.”
- He claimed Google staff had previously described losing the company’s search default status on mobile as a “code red situation”.
- Google’s counterargument said that despite commanding 90% of the search market share, it faces competition from companies like Amazong, Microsoft’s Bing and Yelp.
- Google attorney John Schmidtlein, added: “There are lots of way users access the web other than default search engines, and people use them all the time.”
Google calls its competition ‘inferior’ (Sept. 12)
- Google’s lawyer, Schmidtlein, argued in court that the government is pursuing a regressive lawsuit.
- He said the claims was “all in the hopes that forcing people to use inferior products in the short run will somehow be good for competition in the long run.”
Google’s search engine default status on phones was a ‘priority’ (Sept. 13)
- Chris Barton, who worked for Google from 2004 to 2011, said negotiating deals to make Google the default search engine on mobile devices was a top priority during his time at the company.
- He claimed that in return for default status, phone service providers and manufacturers were guaranteed a portion of ad click revenue.
- This strategy, central to the government’s antitrust case, aimed to establish Google as the primary search engine across various devices, reports News Bytes.
Google faced competition to become default search engine on mobile (Sept. 13)
- Former Googler, Barton, emphasized that Google faced competition from other search engines in becoming the default choice for phone companies during his testimony,.
- In a 2011 email exchange, Google executives observed that AT&T had selected Yahoo as its default search engine, while Verizon had opted for Microsoft’s Bing.
- Barton testified that he encountered a challenge because mobile carriers were fixated on revenue share percentages.
- He aimed to convince potential partners that Google’s high-quality searches would lead to more clicks and greater advertising revenue, even with a lower percentage share.
Googlers were told to be mindful of their language (Sept. 13)
- Google staff were allegedly told back as far as 2023 to avoid using certain terms to avoid being perceived as “monopolists”.
- A memo written by Google Chief Economist Hal Varian read: “We have to be sensitive about antitrust considerations…We should be careful about what we say in both public and private.”
- Staff were told to avoid terms like “market share” and “bundle”.
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Verdict. U.S. District Judge Amit Mehta isn’t expected to issue a ruling until early next year. If he decides Google broke the law, another trial will decide what steps should be taken to rein in the Mountain View, California-based company.
Why we care: If the US Government wins this case, it could mean Google is no longer automatically installed as the default search engine on everyday products, which could threaten its position as the world’s search leader. This means rival companies like Yahoo could realistically stand a chance of taking Google’s crown for the first time, which could bring significant changes to the search landscape as we know it.
What’s at stake. The U.S. and state allies are not asking for money; they want a court order to stop Google from its alleged unfair practices. This order could greatly affect Google’s business. For example:
- The court could potentially split up the company as a solution.
- On a broader scale, the Justice Department might argue that it aims to prevent Google from using its alleged search monopoly to secure exclusive deals in new markets, like AI.
This lawsuit is considered one of the most significant challenges to the tech industry’s dominance since the DOJ sued Microsoft in 1998 for its control of the personal computer market. In that case, the trial court ruled that Microsoft had unlawfully attempted to hinder the rival browser Netscape Navigator. Microsoft ultimately reached a settlement that didn’t break up the company.
If Google’s lead attorney Schmidtelein looks familiar, that may be because he represented Microsoft against the DOJ in the 1998 trial.
Deep dive. Read the US Justice Department’s official statement for more information on why it is suing Google.
The post Google search antitrust trial updates: Everything you need to know (so far) appeared first on Search Engine Land.
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Saturday, September 16th, 2023
Microsoft has been criticised after publishing an AI-generated obituary for NBA star Brandon Hunter.
The former Boston Celtics and Orlando Magic player passed away suddenly this week, aged 42, after collapsing during a hot yoga class in Orlando, Fla.
Shortly after his passing, fans were shocked to see the father of three described as “useless” in an obituary published on MSN.
The headline read:
- “Brandon Hunter useless at 42.”
Why we care. MSN laid off two dozen editorial staff a few years ago with plans to replace the writers with generative AI, the Guardian reported. This case highlights the importance of not relying solely on AI for generating content due to factual inaccuracies and problematic errors, and the need to ensure that all work produced by AI is supervised by humans. Failure to do so could harm your brand’s reputation as well as negatively impacting your search rankings.
Incomprehensible. While the MSN headline was offensive, the rest of the article was incoherent. It read:
- “Former NBA participant, Brandon Hunter, who beforehand performed for the Boston Celtics and Orlando Magic, has handed away on the age of 42, as introduced by Ohio male’s basketball coach Jeff Boals on Tuesday.”
- “Hunter, initially a extremely regarded high school basketball participant in Cincinnati, achieved vital success as a ahead for the Bobcats.”
Reputational damage. Despite swiftly removing the article from the MSN website, Microsoft was criticized on social media for publishing the offensive content:
What Microsoft is saying. A Microsoft spokesperson told Search Engine Land:
- “The accuracy of the content we publish from our partners is important to us, and we continue to enhance our systems to identify and prevent inaccurate information from appearing on our channels. The story in question has been removed.”
However, the company is yet to officially apologize.
Dig deeper. Futurism broke the news in Microsoft Publishes Garbled AI Article Calling Tragically Deceased NBA Player “Useless”.
Other brands stumbles with AI. We’ve previously reported on a number of brands that have published articles with errors, all of which were lacking in E-E-A-T in different ways:
- Men’s Journal published an AI-generated article, What All Men Should Know About Low Testosterone, that contained bad advice and information.
- BuzzFeed published 44 terrible “AI-assisted” articles.
- Gizmodo published an article on “Star Wars” with numerous factual errors.
- Red Ventures-owned properties (including CNET, BankRate and CreditCards.com) have also leaned heavily into AI-generated content.
As a reminder, Google doesn’t care who – or what – writes your content, as long as that content is helpful and not created to manipulate search results.
The post Microsoft calls deceased NBA player ‘useless’ in AI-written obituary appeared first on Search Engine Land.
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Friday, September 15th, 2023
Amazon is rolling out enhanced AI capabilities to help advertisers create better product listings.
The new technology simplifies how sellers create product descriptions, titles, and listing details by automatically generating content based on brief product descriptions.
Previously, sellers were required to enter many pieces of information to create product listings, however, the process has now been streamlined into just one step.
Why we care. Creating product titles, bullet points and descriptions has historically been a time-consuming task for sellers that demanded substantial effort. AI adoption could help alleviate this workload, simplifying and speeding up the process of listing new products, while also potentially improving product listing content.
How it works. To get started, you need to provide a brief description of your product in a few words or sentences. Amazon will then create content for you – which you can then review.
If you wish, you can make further adjustments to the generated content, or you can submit it directly to the Amazon catalog as it is.
Reviews. Early feedback collected over the last few months shows that most sellers are using the content generated by the AI model for their listings without editing it at all.
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What has Amazon said? Robert Tekiela, vice president of Amazon Selection and Catalog Systems, said in a statement:
- “With our new generative AI models, we can infer, improve, and enrich product knowledge at an unprecedented scale and with dramatic improvement in quality, performance, and efficiency.”
- “Our models learn to infer product information through the diverse sources of information, latent knowledge, and logical reasoning that they learn. For example, they can infer a table is round if specifications list a diameter or infer the collar style of a shirt from its image.”
Deep dive. Read Amazon’s announcement in full for more information.
The post Amazon sellers can now use AI to write product titles, descriptions and listing details appeared first on Search Engine Land.
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Friday, September 15th, 2023
Google has begun to push out an update to its helpful content system, the last helpful content system update was the December 2022 helpful content update. The update started on September 14 and should take about two weeks to fully roll out.
What is new. Google said, “The September 2023 helpful content update is rolling out with an improved classifier. It will take about two weeks to complete. We’ll update our ranking release history page when the rollout is complete.”
Google added new details about helpful content, specifically:
- Added new guidance about hosting third-party content and more explanation on what to do after a helpful content system update (perhaps you don’t need to do anything, or perhaps self-assess your content).
- Added new points about removing content or changing dates to the help page on how to create helpful, reliable people-first content.
- Google also removed the words “written by people” and just wrote “helpful content created for people in search results.” I suspect this is to say AI-generated content is fine when it is helpful. Here is a screenshot of the before and after.
In May, Google told us a helpful content system update would be coming this year. Google said this update would enable the helpful content system to “more deeply understands content created from a personal or expert point of view.” “We’re also improving how we rank results in Search overall, with a greater focus on content with unique expertise and experience,” Google also said a few months ago.
Google said this is not today’s update. Google wrote, “This work is still continuing and is not part of this particular update. We’ll share more about our work in this area in the future.”
What to do if you are hit. Google has provided a list of questions you can ask yourself about your content. Read through those questions as we posted over here, and in an unbiased manner, ask yourself if your content is in sync with this update.
Please note if this update has hit you, it can take several months to recover if you do everything right and make changes to your content over time.
More on the helpful content update. Google’s helpful content update specifically targets “content that seems to have been primarily created for ranking well in search engines rather than to help or inform people.”
This algorithm update aims to help searchers find “high-quality content,” Google told us. Google wants to reward better and more useful content that was written for humans and to help users.
Searchers get frustrated when they land on unhelpful webpages that rank well in search because they were written for the purpose of ranking in search engines. This is the type of content you might call “search engine-first content” or “SEO content.”
Google’s helpful content algorithm aims to downgrade those types of websites while promoting more helpful websites, designed for humans, above search engines.
Google said this is an “ongoing effort to reduce low-quality content and make it easier to find content that feels authentic and useful in search.”
Why we care. If you notice any ranking and visibility changes in Google search over the next two weeks or so, especially if those were big changes, you can likely attribute it to this update. Read Google’s advice, make the necessary changes, and hope for a recovery in the upcoming months.
We hope you all will see a positive trend with your ranking and visibility in Google Search from this update.
The post Google September 2023 helpful content system update rolling out appeared first on Search Engine Land.
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Friday, September 15th, 2023
TikTok now serves Wikipedia snippets in some of its search results.
This is the first time that the platform has offered its users results from the wider web as historically, it exclusively featured its own content in SERPs.
The Wikipedia snippets were first noticed in TikTok SERPs by The Verge, as shown here:
A spokesperson for the social media app confirmed that the new feature has been live for several months, however, a formal announcement was never made.
Why we care. While Google has previously stated that it sees TikTok as a search competitor, the social media app is clearly intensifying its efforts in the search arena.
A recent survey revealed the majority of Gen Z women are favoring TikTok over Google for their search needs. This shift in user preference raises questions about whether Google might find itself facing a more concerning competitor in the search space than previously anticipated.
How it works. Wikipedia snippets are served on some accounts for select SERPs for”
The snippets been spotted wedged between relevant videos as users have been scrolling down through in-app search results pages.
By clicking on the links that appear at the bottom of the snippet, users are taken to different sections of the Wikipedia entry.
Deep dive. Read TikTok’s Search and Discover guidelines for more information.
The post TikTok quietly adds Wikipedia snippets to its search results appeared first on Search Engine Land.
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Friday, September 15th, 2023
Amazon is rolling out new features to improve its search capabilities on mobile.
In a bid to rival search engines like Google and Pinterest, the retail giant is launching new search and discovery tools, including:
- Multimodal search (the ability to search using both text and images)
- Expanded AR features
- Find-on-Amazon (a new tool that identifies similar products to those in a photo you share directly with the Amazon app)
Why we care. Although each update may seem inconsequential on its own, together, these new tools can influence how consumers search for products and redirect more searches to Amazon. Consequently, these changes could affect Google’s advertising revenue, as Amazon has been closing in on the dominant position held by Google and Meta in digital ad spending.
Multimodal search. This new tool, which is an improvement on Amazon’s existing visuals search engine, allows consumers to add text as well as images to search for matching products. For example, if you need a replacement part for an appliance, you can snap a photo of the part and include the appliance’s name, such as “Frigidaire.”
Expanded AR efforts. Amazon’s augmented reality tool, initially used for visualizing furniture and decor in your space, now extends to smaller items such as lamps and appliances such as toasters and coffee machines. With this AR feature, you can also rearrange items from one surface to another.
Find-on-Amazon. With this tool, you can search for products using a photo from anywhere. If you see something you like on social media, while browsing the web, reading emails, or chatting online, you can tap the “Share” button and send the image to the Amazon Shopping app. This allows the app to find visually similar products even if you don’t know their name or how to describe them.
More updates. Amazon announced that in addition to the changes mentioned above, it is also rolling out other smaller improvements, including:
- The introduction of sales trend data next to listings (for example, how many items were bought in the past month).
- An improved way to easily search for previously purchased items.
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What has Amazon said? An Amazon spokesperson said in a statement:
- “We are always testing new features and honing existing ones to see what works best to help customers in every phase of their shopping journey.”
- “We know that customer trust is hard to win and easy to lose, so we pay close attention to customer feedback about the shopping experience on Amazon.”
- “We’re always experimenting with ways to make it even easier to shop our store. This means that sometimes things may look different as we test new features, but our mission remains the same: to help you find what you’re looking for.”
Deep dive. Read Amazon’s Search and Shop announcement in full for more information.
The post Amazon launches new search functions on mobile to rival Google appeared first on Search Engine Land.
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Thursday, September 14th, 2023
Search is hard, as Seth Godin wrote in 2005.
I mean, if we think SEO is hard (and it is) imagine if you were trying to build a search engine in a world where:
- The users vary dramatically and change their preferences over time.
- The technology they access search advances every day.
- Competitors nipping at your heels constantly.
On top of that, you’re also dealing with pesky SEOs trying to game your algorithm gain insights into how best to optimize for your visitors.
That’s going to make it a lot harder.
Now imagine if the main technologies you need to lean on to advance came with their own limitations – and, perhaps worse, massive costs.
Well, if you’re one of the writers of the recently published paper, “End-to-End Query Term Weighting” you see this as an opportunity to shine.
What is end-to-end query term weighting?
End-to-end query term weighting refers to a method where the weight of each term in a query is determined as part of the overall model, without relying on manually programmed or traditional term weighting schemes or other independent models.
What does that look like?
Here we see an illustration of one of the key differentiators of the model outlined in the paper (Figure 1, specifically).
On the right side of the standard model (2) we see the same as we do with the proposed model (4), which is the corpus (full set of documents in the index), leading to the documents, leading to the terms.
This illustrates the actual hierarchy into the system, but you can casually think of it in reverse, from the top down. We have terms. We look for documents with those terms. Those documents are in the corpus of all the documents we know about.
To the lower left (1) in the standard Information Retrieval (IR) architecture, you’ll notice that there is no BERT layer. The query used in their illustration (nike running shoes) enters the system, and the weights are computed independently of the model and passed to it.
In the illustration here, the weights are passing equally among the three words in the query. However, it does not have to be that way. It’s simply a default and good illustration.
What is important to understand is that the weights are assigned from outside the model and entered it with the query. We’ll cover why this is important momentarily.
If we look at the term-weight version on the right side, you’ll see that the query “nike running shoes” enters BERT (Term Weighting BERT, or TW-BERT, to be specific) which is used to assign the weights that would be best applied to that query.
From there things follow a similar path for both, a scoring function is applied and documents are ranked. But there’s a key final step with the new model, that is really the point of it all, the ranking loss calculation.
This calculation, which I was referring to above, makes the weights being determined within the model so important. To understand this best, let’s take a quick aside to discuss loss functions, which is important to really understand what’s going on here.
What is a loss function?
In machine learning, a loss function is basically a calculation of how wrong a system is with said system trying to learn to get as close to a zero loss as possible.
Let’s take for example a model designed to determine house prices. If you entered in all the stats of your house and it came up with a value of $250,000, but your house sold for $260,000 the difference would be considered the loss (which is an absolute value).
Across a large number of examples, the model is taught to minimize the loss by assigning different weights to the parameters it is given until it gets the best result. A parameter, in this case, may include things like square feet, bedrooms, yard size, proximity to a school, etc.
Now, back to query term weighting
Looking back at the two examples above, what we need to focus on is the presence of a BERT model to provide the weighting to the terms down-funnel of the ranking loss calculation.
To put it differently, in the traditional models, the weighting of the terms was done independent of the model itself and thus, could not respond to how the overall model performed. It could not learn how to improve in the weightings.
In the proposed system, this changes. The weighting is done from within the model itself and thus, as the model seeks to improve it’s performance and reduce the loss function, it has these extra dials to turn bringing term weighting into the equation. Literally.
ngrams
TW-BERT isn’t designed to operate in terms of words, but rather ngrams.
The authors of the paper illustrate well why they use ngrams instead of words when they point out that in the query “nike running shoes” if you simply weight the words then a page with mentions of the words nike, running and shoes could rank well even if it’s discussing “nike running socks” and “skate shoes”.
Traditional IR methods use query statistics and document statistics, and may surface pages with this or similar issues. Past attempts to address this focused on co-occurrence and ordering.
In this model, the ngrams are weighted as words were in our previous example, so we end up with something like:
On the left we see how the query would be weighted as uni-grams (1-word ngrams) and on the right, bi-grams (2-word ngrams).
The system, because the weighting is built into it, can train on all the permutations to determine the best ngrams and also the appropriate weight for each, as opposed to relying only on statistics like frequency.
Zero shot
An important feature of this model is its performance in zero-short tasks. The authors tested in on:
- MS MARCO dataset – Microsoft dataset for document and passage ranking
- TREC-COVID dataset – COVID articles and studies
- Robust04 – News articles
- Common Core – Educational articles and blog posts
They only had a small number of evaluation queries and used none for fine-tuning, making this a zero-shot test in that the model was not trained to rank documents on these domains specifically. The results were:
It outperformed in most tasks and performed best on shorter queries (1 to 10 words).
And it’s plug-and-play!
OK, that might be over-simplifying, but the authors write:
“Aligning TW-BERT with search engine scorers minimizes the changes needed to integrate it into existing production applications, whereas existing deep learning based search methods would require further infrastructure optimization and hardware requirements. The learned weights can be easily utilized by standard lexical retrievers and by other retrieval techniques such as query expansion.”
Because TW-BERT is designed to integrate into the current system, integration is far simpler and cheaper than other options.
What this all means for you
With machine learning models, it’s difficult to predict example what you as an SEO can do about it (apart from visible deployments like Bard or ChatGPT).
A permutation of this model will undoubtedly be deployed due to its improvements and ease of deployment (assuming the statements are accurate).
That said, this is a quality-of-life improvement at Google, that will improve rankings and zero-shot results with a low cost.
All we can really rely on is that if implemented, better results will more reliably surface. And that’s good news for SEO professionals.
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Thursday, September 14th, 2023
On average, Americans now check their phones 262 times a day and daily screen time averages over five hours per day. People are using their phones more frequently, for longer periods of time, and are shopping from their phones more than ever before.
With 54% of web traffic coming from mobile, adding text messaging (also known as SMS) to your ecommerce stack can help your team reach more customers faster—while maintaining a seamless customer experience.
This practical SMS marketing guide from Cordial provides best practices and essential tips to help you build an effective SMS marketing strategy that drives new revenue streams and loyalty.
Visit Digital Marketing Depot to download Strengthen Customer Loyalty with SMS Marketing.
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Thursday, September 14th, 2023
Finish 2023 strong and step into 2024 ready to stay ahead of Google algorithm updates, supercharge your funnel with qualified leads, and leverage generative AI to your advantage: Attend SMX Next, online November 14-15, to learn actionable, brand-safe search marketing tactics for free without leaving your desk.
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- Integrating generative AI into your content creation process
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Thursday, September 14th, 2023
Google Search Generative Experience (SGE) is looming over the search industry, but little is understood about its mechanisms and expected impact on our organic traffic.
Join this webinar to understand how to overcome the Google SGE challenge. The first step is assessing how Google SGE goes live and how it will impact your organic traffic. Next, identify how to recover from expected traffic drops. Register now to learn about solutions that will help your organization mitigate the risk of SGE.
Register and attend “Overcoming the Google SGE Challenge: Assessment and Recovery Strategies” presented by AgileSEO.
Click here to view more Search Engine Land webinars.
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